我正在尝试构建一个带有自定义训练循环的张量流模型，以使用预测来提供下一个时间步长的输入。关于头部和输入集的模型。我无法找到如何传递我的 2 个输入并管理如何处理自定义调用函数。
我使用这个参考来构建我的代码： https://www.tensorflow.org/tutorials/structured_data/time_series#advanced_autoregressive_model 这是我的数据示例以及我想在每个时间步迭代中使用它的方式。
Input#1
|---input warm up/observed data---|--- forcast---------------------|
t-5 t-4 t-3 t-2 t-1 t-0 t+1 t+2 t+3
var#1 3 2 1 5 4 2 1 3 4
var#2 1 -1 -3 1 0 -5 -11 0 1
var#3 66 67 69 71 73 75 ? ? ?
Using this exemple, var1-2 are inputs, var3 inputs AND labels. Var3 is know on first times steps with a lag on 1 day (today we know last day value of var3) to start model.
First itteration I get these inputs and try forecaste a values that will be insert in var3 t+1 fore the next itteration:
t-5 t-4 t-3 t-2 t-1 t-0 t+1
var#1 3 2 1 5 4 2
var#2 1 -1 -3 1 0 -5
var#3 66 67 69 71 73 75 ?
if ? =88 the next inputs for next itteration will looklike this:
t-5 t-4 t-3 t-2 t-1 t-0 t+1 t+2
var#1 3 2 1 5 4 2 1
var#2 1 -1 -3 1 0 -5 -11
var#3 66 67 69 71 73 75 ?=88 ?
Input#2
Is just a seq of 3 values for the second head of model using only dense layer.
class FeedBack(tf.keras.Model):
def __init__(self, num_timesteps_in, num_timesteps_out, nb_features, nb_attributs,
nb_lstm_units, nb_dense_units):
super(FeedBack, self).__init__()
self.num_timesteps_in = num_timesteps_in
self.num_timesteps_out = num_timesteps_out
self.nb_features = nb_features
self.nb_attributs = nb_attributs
self.nb_lstm_units = nb_lstm_units
self.nb_dense_units = nb_dense_units
self.lstm_cell = tf.keras.layers.LSTMCell(nb_lstm_units)
self.dense = tf.keras.layers.Dense(nb_lstm_units)
def call(self, inputs, training=None):
predictions = []
inputs1 = tf.keras.Input(shape=(self.num_timesteps_in + self.num_timesteps_out, self.nb_features))
inputs2 = tf.keras.Input(shape=(self.nb_attributs))
# Run prediction steps by step with a rolling window on the inputs
for i in range(0, self.num_timesteps_out):
input_chunk = inputs1[i:i + self.num_timesteps_in, :]
# Execute one step.
extraction_info = self.lstm_cell(self.nb_lstm_units,
training=training,
return_sequences=False,
stateful=False)(input_chunk)
extraction_info = tf.keras.layers.Dropout(0.2)(extraction_info)
# -------------- Merge les inputs météo/apports avec les attributs physiographiques --------------------
merged_input = tf.keras.layers.Concatenate(axis=1, name='merged_head')([extraction_info, inputs2])
merged_input = self.Dense(self.nb_dense_units)(merged_input)
merged_input = tf.keras.layers.Dropout(0.2)(merged_input)
prediction = self.Dense(1, activation='linear')(merged_input)
# insert this forecast into the input1 on the next timestep
inputs1[i + self.num_timesteps_in + 1, -1] = prediction
# Add the prediction to the record for extracting at the end.
predictions.append(prediction)
return predictions
现在格式化为 tf.数据集训练模型
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rates[1])
# inputs convertion to tf.data.dataset
inputs_hydro = tf.data.Dataset.from_tensor_slices((X1))
inputs_static = tf.data.Dataset.from_tensor_slices((X2))
output = tf.data.Dataset.from_tensor_slices((y))
combined_dataset = tf.data.Dataset.zip(((inputs_hydro, inputs_static), output))
input_dataset = combined_dataset.batch(5)
base_model = cr.FeedBack(10, 5, 2, 3, 50, 50)
base_model.call = cr.call
model.compile(optimizer=optimizer, loss=tf.keras.losses.MeanSquaredError())
history = model.fit(input_dataset,
verbose=0,
epochs=10)
At this point, I try a lot of thing and read many exemples and questions but still be unable to make it run.The actual version I got this error:
File "E:\Anaconda3\envs\tf2.7_bigData\lib\site-packages\keras\engine\training.py", line 1160, in train_function *
return step_function(self, iterator)
TypeError: tf__call() missing 1 required positional argument: 'inputs'
Any help will be realy welcom! It is my first use of tf.data.dataset and of custom layer/model, it is a hard step in my learning lol